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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 21–30 of 1277 papers

TitleStatusHype
Thinking Out of the Box: Hybrid SAT Solving by Unconstrained Continuous Optimization—0
Learning Distributions over Permutations and Rankings with Factorized Representations—0
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees—0
LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning—0
Generalizable Heuristic Generation Through Large Language Models with Meta-Optimization—0
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
RedAHD: Reduction-Based End-to-End Automatic Heuristic Design with Large Language Models—0
Learning for Dynamic Combinatorial Optimization without Training Data—0
Structured Reinforcement Learning for Combinatorial Decision-MakingCode1
Demand Selection for VRP with Emission QuotaCode0
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